William Lee
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Web
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Challenge: To find in-depth knowledge for a particular entity on the web.

Example: I type in "Microsoft", and I want to find the earnings, 
revenue, and locations for the Microsoft corporation.

Users: Researcher, stock analysis, people who work in human resources, 
and all who want to do research on a person, company, or a particular topic.

Data: The complete web

Method: The semantic web may be the solution to this problem.  However, 
it may be still useful to disambiguate among entities with common names 
and cluster the pages, then do summarization on them.

Email
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Challenge: To extract the major discussion topics within the newsgroup, 
a list of FAQs, and the most active and knowledgeable contributers in a 
newsgroup or mailing list.

Users: People that utilize a technical newsgroup, a company that wants 
to gather a list of most common occuring problems in a product, 
technical support departments that want to construct knowledge bases for 
better customer support.

Data: emails from the newsgroup or mailing list

Method: One may think clustering and summarization of the cluster may be 
the solution to this, but in the newsgroup and mailing list there are 
more specific challenges and interesting ways to exploit IR techniques.

Literature
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Challenge: To find and discover how a topic has evolved through time

Users: Researchers in different fields, managers who want to streamline 
the company's process by looking for inefficiencies, etc.

Data: Scientific literature, company documents

Method: In the simpliest sense, it may be interesting to find the 
function parent_of(A,B) where A and B are documents and much of the 
content of B comes from (or influenced by) A.  With this function and a 
timestamp for each document, it should be possible to create a timeline 
that shows the lineage of a concept.